BeiDou navigation satellite system with global coverage(BDS-3)has been fully operational since July 2020,currently providing the positioning,navigation and timing service together with regional BDS-2.In addition to th...BeiDou navigation satellite system with global coverage(BDS-3)has been fully operational since July 2020,currently providing the positioning,navigation and timing service together with regional BDS-2.In addition to the legacy signals of B1I and B3I,the BDS-3 also transmits several new signals such as BIC,B2a and B2b,which brings new opportunities for rapid ambiguity resolution(AR)of BDS precise point positioning(PPP).In this contribution,a multi-frequency(MF)rapid PPP-AR method with regional network augmentation was proposed.Firstly,BDS five-frequency observations were introduced into uncombined double-differenced models to retrieve regional augmentation corrections at the server.Thereafter,a cascade PPP-AR strategy using extra-wide-lane,wide-lane and narrow-lane ambiguity was employed at the user.Once ambiguities were fixed to integers,the phase correction accuracy could reach about 3 cm on average overall BDS frequencies in the network with inter-station distances of 100-200 km.Subsequently,the statistical results of seven-day simulated kinematic experiments showed that over 99% of epochs on average realized PPP-AR.Correspondingly,the positioning accuracy of the MF fixed solution reached 1.8,1.9,4.7 cm in the east,north and up components,respectively,improving by 5-15% compared with the dual-frequency scheme.Moreover,several vehicle-borne experiments under different urban scenarios were also conducted.Under an open-sky or a relatively open highway scene,the BDS-MF scheme similarly exhibited good performance,and over 98% of epochs achieved rapid PPP-AR with a positioning accuracy better than 3 cm.More encouragingly,for this BDS-challenged experiment with an average satellite number of 8.6,although only 72.06% of epochs were available due to serious signal blockages caused by overpass,tunnels or tall buildings,the horizontal positioning accuracy could still remain 7 cm on average.展开更多
A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of th...A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.展开更多
Underwater acoustic Long-Baseline System(LBL)is an important technique for submarine positioning and navigation.However,the high cost of the seafloor equipment and complex construction of a seafloor network restrict t...Underwater acoustic Long-Baseline System(LBL)is an important technique for submarine positioning and navigation.However,the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area,making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation.We therefore propose an acoustic LBL-based Inertial Measurement Unit(IMU)calibration algorithm.When the underwater vehicle can receive the acoustic signal from a seafloor beacon,the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System(SINS).In this way,the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal.We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration.The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors,and the track line of the underwater vehicle directly affects the accuracy of the calibration results.In addition,we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision.In the experiment,we compare the effects of seven calibration trajectories:straight and diamond-shaped with and without the change of depth,and three sets of curves with the change of depth:circular,S-shaped,and figure-eight.Among them,we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration.We take the maintenance period during which the accumulated SINS Three Dimensional(3D)position errors are below 1 km to evaluate the calibration performance.The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor,the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121%and 38.9%compared to the IMU without calibration and with the laboratory default parameter calibration,indicating the effectiveness of the proposed calibration algorithm.展开更多
基金supported by the National Natural Science Foundation of China[Grant 41774030,Grant 41974027 and Grant 41974029]the Hubei Province Natural Science Foundation of China[Grant 2018CFA081]+1 种基金the frontier project of basic application from Wuhan science and technology bureau[Grant 2019010701011395]the Sino-German mobility programme[Grant No.M-0054].
文摘BeiDou navigation satellite system with global coverage(BDS-3)has been fully operational since July 2020,currently providing the positioning,navigation and timing service together with regional BDS-2.In addition to the legacy signals of B1I and B3I,the BDS-3 also transmits several new signals such as BIC,B2a and B2b,which brings new opportunities for rapid ambiguity resolution(AR)of BDS precise point positioning(PPP).In this contribution,a multi-frequency(MF)rapid PPP-AR method with regional network augmentation was proposed.Firstly,BDS five-frequency observations were introduced into uncombined double-differenced models to retrieve regional augmentation corrections at the server.Thereafter,a cascade PPP-AR strategy using extra-wide-lane,wide-lane and narrow-lane ambiguity was employed at the user.Once ambiguities were fixed to integers,the phase correction accuracy could reach about 3 cm on average overall BDS frequencies in the network with inter-station distances of 100-200 km.Subsequently,the statistical results of seven-day simulated kinematic experiments showed that over 99% of epochs on average realized PPP-AR.Correspondingly,the positioning accuracy of the MF fixed solution reached 1.8,1.9,4.7 cm in the east,north and up components,respectively,improving by 5-15% compared with the dual-frequency scheme.Moreover,several vehicle-borne experiments under different urban scenarios were also conducted.Under an open-sky or a relatively open highway scene,the BDS-MF scheme similarly exhibited good performance,and over 98% of epochs achieved rapid PPP-AR with a positioning accuracy better than 3 cm.More encouragingly,for this BDS-challenged experiment with an average satellite number of 8.6,although only 72.06% of epochs were available due to serious signal blockages caused by overpass,tunnels or tall buildings,the horizontal positioning accuracy could still remain 7 cm on average.
文摘A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.
基金sponsored by“Laoshan Laboratory(No.LSKJ202205100,LSKJ202205104)National Natural Science Foundation of China(41931076)the Young Scholars Program of Shandong University,Weihai.
文摘Underwater acoustic Long-Baseline System(LBL)is an important technique for submarine positioning and navigation.However,the high cost of the seafloor equipment and complex construction of a seafloor network restrict the distribution of the LBL within a small area,making an underwater vehicle difficult for long-distance and high-precision acoustic-based or inertial-based navigation.We therefore propose an acoustic LBL-based Inertial Measurement Unit(IMU)calibration algorithm.When the underwater vehicle can receive the acoustic signal from a seafloor beacon,the IMU is precisely calibrated to reduce the cumulative error of Strapdown Inertial Navigation System(SINS).In this way,the IMU is expected to maintain a certain degree of accuracy by relying solely on SINS when the vehicle reaches out the range of the LBL network and cannot receive the acoustic signal.We present the acoustic LBL-based IMU online calibration model and analyze the factors that affect the accuracy of IMU calibration.The results fulfill the expectation that the gyroscope bias and accelerometer bias are the main error sources that affect the divergence of SINS position errors,and the track line of the underwater vehicle directly affects the accuracy of the calibration results.In addition,we deduce that an optimal calibration trajectory needs to consider the effects of the three-dimensional observability and position dilution of precision.In the experiment,we compare the effects of seven calibration trajectories:straight and diamond-shaped with and without the change of depth,and three sets of curves with the change of depth:circular,S-shaped,and figure-eight.Among them,we find that the figure-eight is the optimal trajectory for acoustic LBL-based IMU online calibration.We take the maintenance period during which the accumulated SINS Three Dimensional(3D)position errors are below 1 km to evaluate the calibration performance.The filed experimental results show that for the Micro-electromechanical Systems-grade IMU sensor,the maintenance period for the IMU calibrated with the proposed algorithm can be increased by 121%and 38.9%compared to the IMU without calibration and with the laboratory default parameter calibration,indicating the effectiveness of the proposed calibration algorithm.
